System and Method for Efficiently Providing a Recommendation

ABSTRACT

A system and method for applying a first filter and a second filter, such as a recommendation and a constraint filter, to a plurality of items, including determining a cost of applying the first filter and the second filter to the plurality of items, and determining an order of applying the first and second filters based on the cost of applying the first and second filters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.09/404,597, filed Sep. 24, 1999 (Notice of Allowance mailed Aug. 1,2008, Supplemental Notice of Allowance mailed Sep. 4, 2008) which isincorporated by reference herein in its entirety.

BACKGROUND

1. Field of the Invention

This invention relates generally to data processing systems, and moreparticularly, collaborative filtering and recommender systems.

2. Background

Recommender systems are becoming widely used in e-commerce businessactivities. For example, systems that make personalized recommendationsare used as a marketing tool to turn “window shoppers” into buyers,increase cross-sells and up-sells, and deepen customer loyalty.Recommender systems allow e-commerce operators to take advantage ofcustomer databases to provide valuable personalized service tocustomers.

Current recommender systems can make generic recommendations tocustomers, but they do not take into account many of the business rulesthat merchandisers wish to implement, such as “don't recommend an itemthat is out of stock,” “don't recommend an item from a category that thecustomer has not selected,” “don't recommend items that are not inseason,” or “don't recommend inappropriate items to minors.” In otherwords, current recommender systems base recommendations solely on thecustomer preference data.

Existing recommender systems allow only the simplest form of filtering,and they do it one of two ways, prefiltering or postfiltering.

Prefiltering requires a constraint system that discovers acceptableitems and then submits all discovered items to a prediction system thatmakes recommendations from this subset. Prefiltering has some seriouspractical limitations, however. For example, gathering the list ofacceptable items is difficult to accomplish efficiently as the list ofacceptable items may be very large since it is selected from the wholeitem catalog.

Postfiltering also requires a system to filter the recommendation list.Postfiltering requires that the recommendation system produce morerecommendations than actually required. The oversized list is passed toa constraint system, which then removes unacceptable items. Althoughpostfiltering may avoid the problem of having to select items from alarge list, it may fail to provide recommendations if the postfilteringeliminates all items.

BRIEF SUMMARY

Methods and systems consistent with the present invention provide arecommendation server that receives a recommendation request from a userof a client computer. The recommendation server contains software toprovide recommendations to the user. To provide the recommendations, therecommendation server applies a constraint filter and a recommendationfilter to a set of items.

In accordance with methods and systems consistent with the presentinvention, a method for providing a recommendation list specifies aconstraint filter to select items satisfying a constraint, selects theitems that satisfy the constraint filter, computes predicted valuesbased on a recommendation filter, and appends the items meetingpredetermined criteria.

In accordance with methods and systems consistent with the presentinvention, a method for applying a recommendation filter and aconstraint filter to a plurality of items is provided. The methodreceives a recommendation request from a user, specifies a constraintfilter to select ones of the items satisfying a constraint, anddetermines the order of the filters based on a cost of the filters. Themethod applies the constraint filter first when the cost of theconstraint filter is lower than the cost of the recommendation filter.Otherwise, the method applies the recommendation filter first when thecost of the recommendation filter is lower than the cost of theconstraint filter.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate an implementation of theinvention and, together with the description, serve to explain theadvantages and principles of the invention. In the drawings,

FIG. 1 depicts a data processing system suitable for practicing methodsand systems consistent with the present invention;

FIG. 2 depicts a more detailed diagram of the client computer depictedin FIG. 1;

FIG. 3 depicts a more detailed diagram of the recommender serverdepicted in FIG. 1;

FIG. 4 depicts a flow chart of the steps performed by the dataprocessing system of FIG. 1 when initiating the constraint processconsistent with methods and systems of the present invention;

FIG. 5 depicts a flow chart of the steps performed by the dataprocessing system of FIG. 1 when initiating the recommender process inaccordance with methods and systems consistent with the presentinvention;

FIG. 6A depicts a constraint tree consistent with methods and systems ofthe present invention;

FIG. 6B depicts an recommendation request form interface consistent withmethods and systems of the present invention;

FIG. 6C depicts an output recommendation list interface consistent withmethods and systems of the present invention; and

FIG. 7 depicts a constraint filter and recommendation filter consistentwith methods and systems of the present invention.

DETAILED DESCRIPTION

The following detailed description of the invention refers to theaccompanying drawings. Although the description includes exemplaryimplementations, other implementations are possible, and changes may bemade to the implementations described without departing from the spiritand scope of the invention. The following detailed description does notlimit the invention. Instead, the scope of the invention is defined bythe appended claims. Wherever possible, the same reference numbers willbe used throughout the drawings and the following description to referto the same or like parts.

Overview

Recommender systems provide recommendations to users based on variousattributes. For example, collaborative filtering (CF) systems are aspecific type of recommender system that recommend items to a user basedon the opinions of other users. In their purest form, CF systems do notconsider the content of the items at all, relying exclusively on thejudgment of humans of the item's value. In this way, CF systems attemptto recapture the cross-topic recommendations that are common incommunities of people.

Commercial applications of ratings-based collaborative filtering nowexist in a variety of domains including books, music, grocery products,dry goods, and information. One example of a CF-system is the GroupLensResearch system that provides a CF for Usenet news and movies. Moreinformation on CF technology may be found at<http://www.netperceptions.com>, hereby incorporated by reference.

To use the recommendation system, an operator may first create aconstraint using a constraint language that allows different businessrules to be described in textual form. For example, to select acandidate from a set of red items, a constraint may be: “candidate isared-thing.” To select a candidate from a set of movies that are bothcomedies and not r-rated, a constraint may be: “candidate isa comedy andnot candidate isa r-rated.”

An item may be anything for which a user may recommend. For example, inthe domain of movies, each movie may be an item. An item may be assignedarbitrarily to one or more categories. For example, a fiction book maybe a member of the “Fiction” category. Category membership may representany attribute of a user or item. For example, an item that is in stockmay be a member of the “in stock” category or an item that is red may bea member of the “red-things” category. This type of categorizationallows the recommendation system to apply a constraint filter based onany attribute or combination of attributes of the item. A constraintfilter is a software with a complex boolean expression as an attributethat the recommendation system uses to restrict items.

A constraint may also consist of free variables. A free variable is aplaceholder for an attribute that can be determined at execution time.For example, to provide the user with the ability to choose a categorywhen applying a constraint, a constraint may be: “candidate isa X,”where the user inputs X at runtime.

Once the operator creates the constraint, the recommendation system maybegin accepting recommendation requests from a user. To use therecommendation system, a user may access a web site with instructionsand web pages for the user to fill out to use the recommendation system.Upon accessing the web site, the user enters a recommendation requestincluding values for various free variables and the number of itemsdesired. The recommendation request may include the values for the freevariables for the constraint filter, the number of items desired anduser information. One skilled in the art will appreciate that othermethods may be used to obtain a recommendation request, such as atelephone call center or manual entry.

Once the recommendation system receives the recommendation request, thesystem next determines the order of a constraint filter and arecommendation filter to apply to the received recommendation request.The constraint filter determines whether an item satisfies a constraintand the recommendation filter determines a predicted value of the itembased on historical or statistical information. One type ofrecommendation filter is the well-known collaborative filtering (CF)technique.

The recommendation filter may compute a predicted value to determine ifan item should be recommended. A predicted value is a number that ratesan item according to certain criteria. For example, a predicted valuemay be used to rank an item based on recommendations from similar usersof the recommendation system. The predicted value is essentially anestimate of how much a user is likely to enjoy an item and may bedetermined, for example, by a CF technique. One skilled in the art willappreciate that the predicted value may be determined in a number ofdifferent ways, such as previous purchases, previous comments or aparticular rating given by the user.

To determine the order of filters to apply, the recommendation systemdetermines the cost of applying successively each filter to all items.The cost of applying each filter is explained below. For example, if thecost to apply a constraint filter before a recommendation filter islower than the other sequence, the recommendation system may choose toapply the constraint filter first. Preferably, the recommendation systemprovides a recommendation to a user with the lowest cost.

Once the order of the filters is determined, the recommendation systemrecommends a list of items to the user that the user may be interestedin based on the recommendation filter and also passes the constraintfilter. If the cheapest method is to apply the constraint filter first,each time an item passes the constraint, it becomes a potentialcandidate for a recommendation list. The candidate is then passed to therecommendation filter. If the candidate passes the recommendationfilter, the candidate and the candidate's predicted value are appendedto a recommendation list.

System Components

FIG. 1 depicts a data processing system 100 suitable for practicingmethods and systems consistent with the present invention. Dataprocessing system 100 comprises a client computer 112 connected torecommendation server 120 via a network 130, such as the Internet. Theuser uses client computer 112 to provide various information torecommendation server 120.

Recommendation server 120 transmits and receives web pages from abrowser on client computer 112 using hypertext markup language (HTML),Java or other techniques. These web pages may-include-images orinstructions to obtain recommendation requests from a user.Recommendation server 120 also contains a database that stores variousdata, such as constraint filters, recommendation filters and items,further described below.

Although only one client computer 112 is depicted, one skilled in theart will appreciate that data processing system 100 may contain manymore client computers and additional client sites. One skilled in theart will also appreciate that client computer 112 may come with therecommendation server software already installed.

FIG. 2 depicts a more detailed diagram of client computer 112, whichcontains a memory 220, a secondary storage device 230, a centralprocessing unit (CPU) 240, an input device 250, and a video display 260.Memory 220 includes browser 222 that allows users to interact withrecommendation server 120 by transmitting and receiving files. Anexample of a browser suitable for use with methods and systemsconsistent with the present invention is the Netscape Navigator browser,from Netscape.

As shown in FIG. 3, recommendation server 120 includes a memory 310, asecondary storage device 320, a CPU 330, an input device 340, and avideo display 350. Memory 310 includes recommendation software 312,which determines if an item should be recommended to the user byapplying a constraint filter 316 and a recommendation filter 318,described below. Recommendation software 312 also interacts with clientcomputer 112 for transmitting and receiving files, such as HTML files orJava files. To interact with client computer 112, recommendationsoftware may include a web server. Although a web server is described inthis particular embodiment of the recommendation server, recommendationserver 120 may interact with a client in other ways such as, voiceprompts, call centers, or kiosks. Memory 310 also includes constraintbuilder software 314, which creates constraints that are used byrecommendation software 312 to recommend an item to the user.Recommendation software 312 and constraint builder software 314 alsoprovide access to database 322 in secondary storage device 320.

Secondary storage device 320 includes grammar file 322 containing a setof rules which map textual constraints to their internal representationin the constraint filter. Secondary storage device also includesdatabase 324 with constraint table 326 that stores built constraints touse with recommendation software 312 and item table 328 with attributeinformation about each item. For example, item table 328 could store acategory identification, item number, and number in stock.

Although aspects of the present invention are described as being storedin memory, one skilled in the art will appreciate that these aspects maybe stored on or read from other computer-readable media, such assecondary storage devices, like hard disks, floppy disks, and CD-ROM; acarrier wave received from a network like the Internet; or other formsof ROM or RAM. Additionally, although specific components and programsof client computer 112 and recommendation server 120 have beendescribed, one skilled in the art will appreciate that these may containadditional or different components or programs.

Constraint Creation Process

FIG. 4 depicts a flow chart of the steps performed when creating aconstraint on recommendation server 120. The constraint creation processis initiated, for example, by an operator inputting a textual constraintinto constraint builder software 314 (step 402). The constraint maycontain free variables or bound expressions. After the operator inputsthe constraint, the builder software checks the syntax of the constraint(step 404). For example, an acceptable syntax may include logicalexpressions or relational expression. That is, constraint buildersoftware verifies that the operator created a valid constraint tosignify a possible business rule. Logical expression include, forexample, AND, OR, or NOT boolean expressions. Relational expressionsinclude, for example EQUAL TO, GREATER THAN, LESS THAN or ISA.

Once the constraint is verified for the correct syntax, constraintbuilder software may translate the textual constraint into, for example,a constraint in a tree structure format (step 406). Constraint buildersoftware 314 includes the well-known yacc parser to translate thetextual constraint. The builder software reads grammar specificationfile 322 and generates a constraint tree consisting of objects by usingthe grammar specifications applied to the textual constraint. The newconstraint tree is in a format acceptable to recommendation software312.

Grammar specification file 322 consists of many different object formatsto create the constraint tree, such as logical expression objects,relational expression objects or leaf objects. Logical expressionobjects are a type of boolean expression, such as AND, OR or NOT.Relational expression objects compare two leaf objects. Leaf objectsrepresent any entity in the application domain. A leaf may be a categoryleaf, free variable leaf, candidate leaf, or subject leaf. A categoryleaf represents at least one item in database 324, such as “Thriller” or“Shoes.” A free variable leaf is essentially a placeholder that isspecified at execution time by the user or operator. The free variableleaf stores a variable name, which is bound to an actual entity in theapplication domain during the recommendation process, further describedbelow. Similar to the free variable leaf, the candidate leaf is also aplaceholder. The candidate leaf represents the actual item discovered bythe recommendation filter. Finally, a subject leaf is a placeholder forthe user who requested the recommendation. For example, a user may havean attribute of being 14 years old. Thus, leaf objects in the constrainttree may reflect this attribute.

For example, a textual constraint created by an operator to producerecommendations for movies that are now playing, and are of a genreselected by a user, and prohibits the recommendation of r-rated moviesto minors may be:

X: (candidate isa movie) and (candidate isa X) and (candidate isaNowPlay) and (not (subject isa minor) and (candidate isa R-rated) ).

Constraint builder software 314 would translate the textual constraintto the tree of objects depicted in FIG. 6A.

Once constraint builder software 314 translates the textual constraintto a constraint tree, the constraint tree is placed as an entity inconstraint table 326 (step 408). The constraint tree is used later byrecommendation software 312 during the recommendation process. Thiscompletes the constraint creation process.

Recommendation Process

FIG. 5 depicts a flow chart of the steps performed when initiating therecommendation process in accordance with methods and systems consistentwith the present invention. The recommendation process is initiated, forexample, by a user accessing recommendation server 120 (step 502). Onceaccessed, recommendation software 312 transmits a recommendation requestpage to client computer 112 (step 504). The request page may be in HTML.One skilled in the art will appreciate that the inquiry page may bedesigned in other formats, such as Visual Basic or Java. The requestpage may include a category selection fields 602 and 604, desiredresults field 606 for the user to fill out, and a submit button 608, asshown in FIG. 6B. Once the request page is displayed on browser 222, theuser may select a category and enter the number of results and submitthe request to recommendation server 120 by pressing button 608 (step506). When button 608 is pressed, browser 222 transmits the category andnumber of results information to recommendation server 120 using thewell-known HyperText Transport Protocol (HTTP).

Once received at recommendation server 120, recommendation software 312binds the free variables in the appropriate constraint with the categoryselected by the user and the number of items desired (step 508). To doso, recommendation software 312 first locates the appropriate constraintin constraint table 326. The constraint may be found in constraint table326, for example, by a tag appended to the recommendation requestindicating the constraint. Once located, recommendation software 312descends the constraint tree to locate free variable objects. Once afree variable object is found, recommendation software 312 copies theinformation from the recommendation request to the free variable. Forexample, in FIG. 6B, if a user selected “rock” and “jazz” as thecategories to search, step 508 generates an array of length two thatcontains the object representing jazz and rock with a correspondingindex number. The array looks as follows:

Index Object 1 Jazz 2 Rock

Step 508 uses the array when descending the constraint tree searchingfor a free variable object. When a free variable object is located, theindex is matched and the corresponding object is copied to the freevariable object. Ultimately, recommendation software 312 will examineeach node in the constraint tree to locate all free variables and storecorresponding information in each free variable. Although two freevariables were used in FIG. 6B, one skilled in the art will appreciatethat many more free variables may exist in the recommendation request.

After the free variables in the constraint tree are bound,recommendation software 312 examines each item in item table 328 for anitem to recommend to the user. The process begins with recommendationsoftware determining the lowest cost method to complete a recommendationrequest (step 510). To do so, recommendation software 312 determines thecost of applying constraint filter 316 and recommendation filter 318 indifferent orders to the items. As shown in FIG. 7, each filter has ageneration interface that produces items and a rejection interface thatdetermines whether a particular item is suitable for recommendation tothe user. Each filter is applied sequentially. The generation interfaceis called on the first filter, and the produced items are passed to therejection interface on the second filter.

If the cost of generating a sufficient number of items by applying thegeneration interface of the-constraint filter before applying therejection interface of the recommendation filter is lower than applyingthe generation interface of the recommendation filter before applyingthe rejection interface of the constraint filter, then recommendationsoftware 312 will apply the generation interface of the constraintsystem first to item table 328. Otherwise, recommendation software 312applies the generation interface of the recommendation filter first. Thecost may be approximated by the following equation:

Cost=(number of results required/probability that a randomly selecteditem will pass the rejection interface of the second filter)*(cost ofapplying the generation interface of the first filter to generate asingle item+cost of applying the rejection interface of the secondfilter to a single item)

Once the order of the filters is determined, recommendation software 312determines if enough items have been located (step 512). That is,recommendation software continues to discover new items in item table328 until the required number of items requested from the user has beenreached. Once an item has been discovered in item table 328, the item isevaluated (step 514). Evaluation occurs by applying the constraintfilter to the item. Items that pass the constraint filter will be passedto the recommendation filter (step 516). An item passes the constraintfilter when it satisfies the constraints conditions. If an item does notpass the constraint filter, the item is discarded and not recommended.

Next, the recommendation filter may compute a predicted value for theitem (step 518). Also in step 518, each item whose predicted value is atleast a threshold value is appended to a result list for display onclient computer 112. The results may be displayed in HTML.

FIG. 6C depicts an output interface 620 presented to the user aftersubmitting the recommendation request in FIG. 6B. Output interface 620contains a recommendation list 622. For example, the user may select anitem from the list to purchase.

Conclusion

Methods, systems, and articles of manufacture consistent with thepresent invention provide a recommendation server that receives arecommendation request from a user of a client computer. Therecommendation server contains software to provide recommendations tothe user. To provide the recommendations, the recommendation serverapplies a constraint filter and a recommendation filter on a set ofitems.

The foregoing description of an implementation of the invention has beenpresented for purposes of illustration and description. It is notexhaustive and does not limit the invention to the precise formdisclosed. Modifications and variations are possible in light of theabove teachings or may be acquired from practicing of the invention. Forexample, the described implementation includes software but the presentinvention may be implemented as a combination of hardware and softwareor in hardware alone.

1. A method for applying a recommendation filter and a constraint filterto a plurality of items in a data processing system, comprising thesteps of: receiving a recommendation request; specifying a constraintfilter that selects ones of items satisfying a constraint; determiningthe order of the filters based on a cost of applying the filters,including: applying the constraint filter first when a cost of applyingthe filters when the constraint filter is applied first is lower than acost of applying the filters when the recommendation filter is appliedfirst, and applying the recommendation filter first when the cost ofapplying the filters when the recommendation filter is applied first islower than the cost of applying the filters when the constraint filteris applied first.
 2. The method of claim 1, wherein determining theorder of the filters further includes: analyzing a cost for applying thefilters to the ones of the items; and determining a probability that theones of the items will pass both filters.
 3. The method of claim 1,wherein the cost of applying the filters is determined by the equation:C=M/P(G+R), wherein M is a number of desired items, P is a probabilitythat the ones of the items will pass a second applied filter, G is atime to retrieve the ones of the items, and R is a time to decide if theones of the items will pass a second applied filter.
 4. The method ofclaim 1, further comprising: calculating the cost of applying thefilters based at least on a probability that a randomly selected item ofthe plurality of items will pass a second applied filter of therecommendation and constraint filters.
 5. The method of claim 4, whereinthe calculating is further based on a cost of applying a first appliedfilter of the recommendation and constraint filters to generate a singleitem.
 6. The method of claim 4, wherein the calculating is further basedon a cost of applying a second applied filter of the recommendation andconstraint filters to a single item.
 7. The method of claim 4, whereinthe calculating is further based on a number of results required.
 8. Amethod comprising: determining an order for applying a first filter anda second filter to a plurality of items, comprising calculating a costof applying the first filter and the second filter in a first orderbased at least on (i) a number of results required, (ii) a probabilitythat a randomly selected item of the plurality of items will pass asecond applied filter of the first filter and the second filter, (iii) acost of applying a first applied filter of the first filter and thesecond filter to generate a single item, and (iv) a cost of applying thesecond applied filter of the first filter and the second filter to thesingle item; and generating a recommendation list, comprising applyingthe first filter and the second filter to the plurality of itemsaccording to the determined order.
 9. The method of claim 8, furthercomprising: receiving a recommendation request.
 10. The method of claim8, further comprising: displaying the recommendation list.
 11. Themethod of claim 8, wherein the determining an order comprisescalculating the cost using the equation: cost=(number of resultsrequired/probability that a randomly selected item will pass the secondapplied filter)*(cost of applying the first applied filter to generate asingle item+cost of applying the second applied filter to the singleitem).
 12. A system, comprising: a recommendation server configured to:receive a recommendation request, determine an order for applying afirst filter and a second filter to a plurality of items based on a costof applying the first filter and the second filter, and generate arecommendation list, comprising applying the first filter and the secondfilter in the determined order.
 13. The system of claim 12, wherein thecost is based at least on a probability that a randomly selected item ofthe plurality of items will pass a second applied filter of the firstfilter and the second filter.
 14. The system of claim 12, wherein thecost is based at least on a cost of applying a first applied filter ofthe first filter and the second filter to generate a single item. 15.The system of claim 12, wherein the cost is based at least on a cost ofapplying a second applied filter of the first filter and the secondfilter to a single item.
 16. The system of claim 12, wherein the cost isbased at least on a number of results required.
 17. A computer programproduct comprising a tangible computer readable storage medium havingcontrol logic stored therein, the control logic, when executed, causinga processor to perform a method comprising: receiving a recommendationrequest; determining an order for applying a first filter and a secondfilter to a plurality of items based on a cost of applying the firstfilter and the second filter; and generating a recommendation list,comprising applying the first filter and the second filter in thedetermined order.
 18. The computer program product of claim 17, whereinthe cost is based at least on (i) a number of results required, (ii) aprobability that a randomly selected item of the plurality of items willpass a second applied filter of the first filter and the second filter,(iii) a cost of applying a first applied filter of the first filter andthe second filter to generate a single item, and (iv) a cost of applyingthe second applied filter of the first filter and the second filter tothe single item.
 19. A system, comprising: means for receiving arecommendation request from a user; means for determining an order forapplying a first filter and a second filter to a plurality of itemsbased on a cost of applying the first filter and the second filter; andmeans for generating a recommendation list, comprising applying thefirst filter and the second filter in the determined order.
 20. Thesystem of claim 19, wherein the cost is based at least on (i) a numberof results required, (ii) a probability that a randomly selected item ofthe plurality of items will pass a second applied filter of the firstfilter and the second filter, (iii) a cost of applying a first appliedfilter of the first filter and the second filter to generate a singleitem, and (iv) a cost of applying the second applied filter of the firstfilter and the second filter to the single item.
 21. A method,comprising: sending a recommendation request to a server; receiving arecommendation list generated by the application of a first filter and asecond filter in an order based on a cost of applying the first filterand the second filter.
 22. The method of claim 21, wherein the cost isbased at least on (i) a number of results required, (ii) a probabilitythat a randomly selected item of the plurality of items will pass asecond applied filter of the first filter and the second filter, (iii) acost of applying a first applied filter of the first filter and thesecond filter to generate a single item, and (iv) a cost of applying thesecond applied filter of the first filter and the second filter to thesingle item.